Hair Biomarkers to Support Barren-ground Caribou Health Monitoring and Management
Bibliographic record
Abstract
Barren-ground caribou (Rangifer tarandus groenlandicus) are a keystone species of Canada, whose population health is a current and future management priority. Many of these historically numerous populations, including the Bluenose-East (BNE) and Dolphin and Union (DU) herds, have severely declined in the last two decades, thus there is an impetus to understand the health status of these populations. Considering the challenges associated with monitoring Arctic wildlife, hair is a practically advantageous sample type that is currently opportunistically collected. I evaluated two biomarkers derived from caribou hair (trace element and cortisol concentrations) in the context of opportunistic monitoring and review the literature to understand how to best orient Rangifer health research into management and conservation. First, I reviewed the most abundant health literature on caribou, the Rangifer infectious disease literature, and documented numerous barriers to health information dissemination and implementation. I then outlined practical solutions to facilitate solutions-oriented Rangifer health research. Second, I examined two biomarkers pertinent to caribou health, hair trace element and hair cortisol concentrations that provide seasonal measures of nutrition and contribute to allostatic load, respectively. I demonstrated that these biomarkers vary between anatomic sampling locations and provided recommendations for future hair collection protocols. Furthermore, I uncovered associations of these biomarkers with sex, season, year, and sampling source that have implications for future monitoring and biomarker interpretation. This work has advanced our understanding of two biomarkers derived from caribou hair, outlined future research avenues to improve the robustness of these monitoring tools, and demonstrated broadly how to better translate caribou health research into management and conservation frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".